Rising population density and global mobility are among the reasons why pathogens such as SARS-CoV-2, the virus that causes COVID-19, spread so rapidly across the globe. The policy response to such pandemics will always have to include accurate monitoring of the spread, as this provides one of the few alternatives to total lockdown. However, COVID-19 diagnosis is currently performed almost exclusively by reverse transcription polymerase chain reaction (RT-PCR). Although this is efficient, automatable, and acceptably cheap, reliance on one type of technology comes with serious caveats, as illustrated by recurring reagent and test shortages. We therefore developed an alternative diagnostic test that detects proteolytically digested SARS-CoV-2 proteins using mass spectrometry (MS). We established the Cov-MS consortium, consisting of 15 academic laboratories and several industrial partners to increase applicability, accessibility, sensitivity, and robustness of this kind of SARS-CoV-2 detection. This, in turn, gave rise to the Cov-MS Digital Incubator that allows other laboratories to join the effort, navigate, and share their optimizations and translate the assay into their clinic. As this test relies on viral proteins instead of RNA, it provides an orthogonal and complementary approach to RT-PCR using other reagents that are relatively inexpensive and widely available, as well as orthogonally skilled personnel and different instruments. Data are available via ProteomeXchange with identifier PXD022550.

Rising population density and global mobility are among the reasons why pathogens such as SARS-CoV-2, the virus that causes COVID-19, spread so rapidly across the globe. The policy response to such pandemics will always have to include accurate monitoring of the spread, as this provides one of the few alternatives to total lockdown. However, COVID-19 diagnosis is currently performed almost exclusively by Reverse Transcription Polymerase Chain Reaction (RT-PCR). Although this is efficient, automatable and acceptably cheap, reliance on one type of technology comes with serious caveats, as illustrated by recurring reagent and test shortages. We therefore developed an alternative diagnostic test that detects proteolytically digested SARS-CoV-2 proteins using Mass Spectrometry (MS). We established the Cov-MS consortium, consisting of fifteen academic labs and several industrial partners to increase applicability, accessibility, sensitivity and robustness of this kind of SARS-CoV-2 detection. This in turn gave rise to the Cov-MS Digital Incubator that allows other labs to join the effort, navigate and share their optimizations, and translate the assay into their clinic. As this test relies on viral proteins instead of RNA, it provides an orthogonal and complementary approach to RT-PCR, using other reagents that are relatively inexpensive and widely available, as well as orthogonally skilled personnel and different instruments. Data are available via ProteomeXchange with identifier PXD022550.

This study reports on the development of a novel serum protein panel of three prostate cancer biomarkers, Filamin A, Filamin B and Keratin-19 (FLNA, FLNB and KRT19) using multivariate models for disease screening and prognosis. ELISA and IPMRM (LC-MS/MS) based assays were developed and analytically validated by quantitative measurements of the biomarkers in serum. Retrospectively collected and clinically annotated serum samples with PSA values and Gleason scores were analyzed from subjects who underwent prostate biopsy, and showed no evidence of cancer with or without indication of prostatic hyperplasia, or had a definitive pathology diagnosis of prostatic adenocarcinoma. Probit linear regression models were used to combine the analytes into score functions to address the following clinical questions: does the biomarker test augment PSA for population screening? Can aggressive disease be differentiated from lower risk disease, and can the panel discriminate between prostate cancer and benign prostate hyperplasia? Modelling of the data showed that the new prostate biomarkers and PSA in combination were better than PSA alone in identifying prostate cancer, improved the prediction of high and low risk disease, and improved prediction of cancer versus benign prostate hyperplasia.

Aim:A novel strategy for prostate cancer (PrCa) biomarker discovery is described.Materials & methods:In vitro perturbation biology, proteomics and Bayesian causal analysis identified biomarkers that were validated in in vitro models and clinical specimens.Results:Filamin-B (FLNB) and Keratin-19 were identified as biomarkers. Filamin-A (FLNA) was found to be causally linked to FLNB. Characterization of the biomarkers in a panel of cells revealed differential mRNA expression and regulation. Moreover, FLNA and FLNB were detected in the conditioned media of cells. Last, in patients without PrCa, FLNA and FLNB blood levels were positively correlated, while in patients with adenocarcinoma the relationship is dysregulated.Conclusion:These data support the strategy and the potential use of the biomarkers for PrCa.

Aim: Developing LC–MS methods for biomolecules is often challenging due to issues with molecular size and complexity, nonspecific binding, protein binding, solubility and sensitivity. As a result, complex sample preparation workflows, including immune-affinity and/or protein digestion and lengthy analysis potentially using nano-flow LC, may be needed to achieve the required sensitivity. This work aims to provide a simple, sensitive, fast and robust method for quantification of intact IGF-I from human serum using UPLC–MS/MS. Methods: IGF-I serum samples were denatured with sodium dodecyl sulfate, followed by organic protein precipitation to effectively disrupt protein binding and subsequent SPE of the resulting supernatant for sample cleanup and enrichment prior to LC–MS/MS analysis. Separation was performed on an analytical scale LC using a reversed-phase column containing <2 μm solid core particle followed by detection on a tandem quadrupole MS in multiple reaction monitoring mode. Results: Intact IGF-I was quantified from serum using the method described above at a LLOQ of 5 ng/ml with a dynamic range 5–1000 ng/ml (r2>0.99) and mean accuracy of 101.76%. Accuracies for quality control samples were between 93.9–107.7% with RSD <7%. Conclusion: The analytical sensitivity, linear dynamic range and excellent reproducibility of this method reliably measures endogenous and elevated serum IGF-I levels, demonstrating its utility in discovery, bioanalysis and clinical research.